Tackling the Multi-Objective Vehicle Routing Problem With Uncertain Demands

Abstract

In this article, the single capacitated vehicle routing problem with time windows and uncertain demands is studied. Having a set of customers whose actual demand is not known in advance, needs to be serviced. The goal of the problem is to find a set of routes with the lowest total travel distance and tardiness time, subject to vehicle capacity and time window constraints. Two uncertainty types can be distinguished in the literature: random and epistemic uncertainties. Because several studies focalized upon the random aspect of uncertainty, the article proposes to tackle the problem by considering dominance relations to handle epistemic uncertainty in the objective functions. Further, an epistemic multi-objective local search-based approach is proposed for studying the behavior of such a representation of demands on benchmark instances generated following a standard generator available in the literature. Finally, the results achieved by the proposed method using epistemic representation are compared to those reached by a deterministic version. Encouraging results have been obtained.

Simulation of the Daily Activity Plans of Travelers Using the Park-and-Ride System and Autonomous Vehicles: Work and Shopping Trip Purposes

“…Elgarej et al (2020) developed a routing problem that included a set of activities of an electric vehicle (EV) and charging stations, the authors solved the routing problem by the Ant system algorithm to guide the EVs using routes to their destinations where chagrining stations are closed [17]. A novel contribution has been done by Aïder and Skoudarli (2020), in which the demand was included as a parameter of the method that used an epistemic representation [18]. Rizopoulos and Esztergár-Kiss (2020) developed an approach to model the behavior of electric vehicles concerning transport modes, charging stations, locations of the activities, and the departure times of the travelers [16].…”